Deep Generative Models : Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / / edited by Anirban Mukhopadhyay, Ilkay Oksuz, Sandy Engelhardt, Dajiang Zhu, Yixuan Yuan

• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (136 pages)
• Disciplina: 006.37; 006.31
• Soggetto topico: Computer vision; Machine learning; Education - Data processing; Application software; Computer Vision; Machine Learning; Computers and Education; Computer and Information Systems Applications
• Persona (resp. second.): MukhopadhyayAnirban
• Nota di bibliografia: Includes bibliographical references and index.
• Sommario/riassunto: This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore. DG4MICCAI 2022 accepted 12 papers from the 15 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.
• Titolo autorizzato: Deep generative models
• ISBN: 9783031185762; 3031185765
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910616390203321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 13609